Overview

Dataset statistics

Number of variables16
Number of observations150326
Missing cells329588
Missing cells (%)13.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.2 MiB
Average record size in memory315.3 B

Variable types

NUM11
CAT4
DATE1

Warnings

Fecha has constant value "150326" Constant
Hora has a high cardinality: 59471 distinct values High cardinality
ID_Group is highly correlated with IDHigh correlation
ID is highly correlated with ID_GroupHigh correlation
Neutral has 27774 (18.5%) missing values Missing
Happy has 27774 (18.5%) missing values Missing
Sad has 27774 (18.5%) missing values Missing
Angry has 27774 (18.5%) missing values Missing
Surprised has 27774 (18.5%) missing values Missing
Scared has 27774 (18.5%) missing values Missing
Disgusted has 27774 (18.5%) missing values Missing
Contempt has 27774 (18.5%) missing values Missing
Valence has 27774 (18.5%) missing values Missing
Arousal has 27774 (18.5%) missing values Missing
Heart_Rate has 51848 (34.5%) missing values Missing
Hora is uniformly distributed Uniform
Happy has 37312 (24.8%) zeros Zeros
Sad has 20639 (13.7%) zeros Zeros
Angry has 22881 (15.2%) zeros Zeros
Surprised has 23112 (15.4%) zeros Zeros
Scared has 28871 (19.2%) zeros Zeros
Disgusted has 28225 (18.8%) zeros Zeros
Contempt has 30555 (20.3%) zeros Zeros

Reproduction

Analysis started2020-09-19 16:21:12.930376
Analysis finished2020-09-19 16:21:59.712506
Duration46.78 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Distinct59471
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2020-09-19 00:00:00
Maximum2020-09-19 00:36:23.560000
2020-09-19T11:21:59.888372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:22:00.150547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Neutral
Real number (ℝ≥0)

MISSING

Distinct121312
Distinct (%)99.0%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.5216363836
Minimum0
Maximum0.9761002
Zeros1
Zeros (%)< 0.1%
Memory size1.1 MiB
2020-09-19T11:22:00.490412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.18173331
Q10.404725125
median0.52866585
Q30.6444498
95-th percentile0.842287015
Maximum0.9761002
Range0.9761002
Interquartile range (IQR)0.239724675

Descriptive statistics

Standard deviation0.1889532438
Coefficient of variation (CV)0.3622317188
Kurtosis-0.09451900079
Mean0.5216363836
Median Absolute Deviation (MAD)0.11981095
Skewness-0.1383539582
Sum63927.58209
Variance0.03570332835
MonotocityNot monotonic
2020-09-19T11:22:00.749239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.56217563< 0.1%
 
0.47552833< 0.1%
 
0.43296363< 0.1%
 
0.65912973< 0.1%
 
0.51681973< 0.1%
 
0.37091693< 0.1%
 
0.53083013< 0.1%
 
0.58528553< 0.1%
 
0.50673962< 0.1%
 
0.55110252< 0.1%
 
0.55990592< 0.1%
 
0.88871162< 0.1%
 
0.68864432< 0.1%
 
0.74248332< 0.1%
 
0.50881542< 0.1%
 
0.40771342< 0.1%
 
0.42376422< 0.1%
 
0.49273912< 0.1%
 
0.57197582< 0.1%
 
0.33280712< 0.1%
 
0.40719942< 0.1%
 
0.56194282< 0.1%
 
0.51978122< 0.1%
 
0.5227492< 0.1%
 
0.53139822< 0.1%
 
Other values (121287)12249481.5%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
01< 0.1%
 
7e-091< 0.1%
 
9e-091< 0.1%
 
1.2e-081< 0.1%
 
1.3e-081< 0.1%
 
1.7e-081< 0.1%
 
2.1e-081< 0.1%
 
1.545e-061< 0.1%
 
2.904e-061< 0.1%
 
3.171e-061< 0.1%
 
ValueCountFrequency (%) 
0.97610021< 0.1%
 
0.97608761< 0.1%
 
0.97607611< 0.1%
 
0.97606951< 0.1%
 
0.9760421< 0.1%
 
0.97601521< 0.1%
 
0.97601121< 0.1%
 
0.97592781< 0.1%
 
0.97588611< 0.1%
 
0.97579211< 0.1%
 

Happy
Real number (ℝ≥0)

MISSING
ZEROS

Distinct72091
Distinct (%)58.8%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.07346600264
Minimum0
Maximum0.9953589
Zeros37312
Zeros (%)24.8%
Memory size1.1 MiB
2020-09-19T11:22:01.005568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000102532
Q30.04196257
95-th percentile0.49391828
Maximum0.9953589
Range0.9953589
Interquartile range (IQR)0.04196257

Descriptive statistics

Standard deviation0.1681486587
Coefficient of variation (CV)2.288795533
Kurtosis8.220559269
Mean0.07346600264
Median Absolute Deviation (MAD)0.000102532
Skewness2.897244686
Sum9003.405556
Variance0.02827397141
MonotocityNot monotonic
2020-09-19T11:22:01.233987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03731224.8%
 
1e-0914110.9%
 
2e-097090.5%
 
3e-094510.3%
 
4e-093530.2%
 
5e-092940.2%
 
6e-092410.2%
 
7e-092150.1%
 
8e-091840.1%
 
9e-091700.1%
 
1e-081550.1%
 
1.1e-081370.1%
 
1.3e-081200.1%
 
1.2e-081180.1%
 
1.4e-081110.1%
 
1.5e-081030.1%
 
1.6e-08990.1%
 
1.8e-08880.1%
 
1.7e-08850.1%
 
1.9e-08790.1%
 
2.1e-08790.1%
 
2.3e-08770.1%
 
2e-08770.1%
 
2.6e-0873< 0.1%
 
2.2e-0863< 0.1%
 
Other values (72066)7974853.1%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
03731224.8%
 
1e-0914110.9%
 
2e-097090.5%
 
3e-094510.3%
 
4e-093530.2%
 
5e-092940.2%
 
6e-092410.2%
 
7e-092150.1%
 
8e-091840.1%
 
9e-091700.1%
 
ValueCountFrequency (%) 
0.99535891< 0.1%
 
0.9853991< 0.1%
 
0.98507791< 0.1%
 
0.98345551< 0.1%
 
0.98259051< 0.1%
 
0.98157731< 0.1%
 
0.97949831< 0.1%
 
0.97719951< 0.1%
 
0.97634281< 0.1%
 
0.97503161< 0.1%
 

Sad
Real number (ℝ≥0)

MISSING
ZEROS

Distinct90401
Distinct (%)73.8%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.138953194
Minimum0
Maximum0.9999855
Zeros20639
Zeros (%)13.7%
Memory size1.1 MiB
2020-09-19T11:22:01.512383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.16e-07
median0.007086779
Q30.18245805
95-th percentile0.68456401
Maximum0.9999855
Range0.9999855
Interquartile range (IQR)0.182457134

Descriptive statistics

Standard deviation0.2363820677
Coefficient of variation (CV)1.701163254
Kurtosis3.179557031
Mean0.138953194
Median Absolute Deviation (MAD)0.007086779
Skewness1.965602828
Sum17028.99183
Variance0.05587648191
MonotocityNot monotonic
2020-09-19T11:22:01.779238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02063913.7%
 
1e-0910410.7%
 
2e-095380.4%
 
3e-093770.3%
 
4e-092850.2%
 
5e-092300.2%
 
6e-091920.1%
 
7e-091760.1%
 
8e-091500.1%
 
9e-091350.1%
 
1e-081230.1%
 
1.1e-081130.1%
 
1.2e-08980.1%
 
1.3e-08950.1%
 
1.5e-08880.1%
 
1.4e-08870.1%
 
1.6e-08820.1%
 
1.9e-0869< 0.1%
 
1.7e-0868< 0.1%
 
0.999973368< 0.1%
 
1.8e-0868< 0.1%
 
2.2e-0867< 0.1%
 
0.999973466< 0.1%
 
2.1e-0865< 0.1%
 
2e-0861< 0.1%
 
Other values (90376)9757164.9%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
02063913.7%
 
1e-0910410.7%
 
2e-095380.4%
 
3e-093770.3%
 
4e-092850.2%
 
5e-092300.2%
 
6e-091920.1%
 
7e-091760.1%
 
8e-091500.1%
 
9e-091350.1%
 
ValueCountFrequency (%) 
0.99998557< 0.1%
 
0.999978818< 0.1%
 
0.99997872< 0.1%
 
0.99997861< 0.1%
 
0.99997852< 0.1%
 
0.99997842< 0.1%
 
0.99997831< 0.1%
 
0.99997821< 0.1%
 
0.99997811< 0.1%
 
0.99997791< 0.1%
 

Angry
Real number (ℝ≥0)

MISSING
ZEROS

Distinct85123
Distinct (%)69.5%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.01268409554
Minimum0
Maximum0.6048135
Zeros22881
Zeros (%)15.2%
Memory size1.1 MiB
2020-09-19T11:22:02.053196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.07e-07
median0.000474177
Q30.0090527275
95-th percentile0.072486626
Maximum0.6048135
Range0.6048135
Interquartile range (IQR)0.0090526205

Descriptive statistics

Standard deviation0.03017397178
Coefficient of variation (CV)2.378882411
Kurtosis26.81607792
Mean0.01268409554
Median Absolute Deviation (MAD)0.000474177
Skewness4.284667253
Sum1554.461276
Variance0.0009104685731
MonotocityNot monotonic
2020-09-19T11:22:02.611529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02288115.2%
 
1e-0912290.8%
 
2e-096370.4%
 
3e-094470.3%
 
4e-093330.2%
 
5e-092840.2%
 
6e-092360.2%
 
7e-092070.1%
 
8e-091920.1%
 
9e-091610.1%
 
1.1e-081460.1%
 
1e-081420.1%
 
1.3e-081150.1%
 
1.2e-081140.1%
 
1.5e-081030.1%
 
1.6e-081020.1%
 
1.4e-08990.1%
 
1.9e-08880.1%
 
1.7e-08870.1%
 
1.8e-08810.1%
 
2e-08760.1%
 
2.1e-0874< 0.1%
 
2.3e-0873< 0.1%
 
2.4e-0871< 0.1%
 
2.2e-0871< 0.1%
 
Other values (85098)9450362.9%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
02288115.2%
 
1e-0912290.8%
 
2e-096370.4%
 
3e-094470.3%
 
4e-093330.2%
 
5e-092840.2%
 
6e-092360.2%
 
7e-092070.1%
 
8e-091920.1%
 
9e-091610.1%
 
ValueCountFrequency (%) 
0.60481351< 0.1%
 
0.49240311< 0.1%
 
0.39048031< 0.1%
 
0.38599741< 0.1%
 
0.38520261< 0.1%
 
0.37890071< 0.1%
 
0.37864971< 0.1%
 
0.37747651< 0.1%
 
0.37613251< 0.1%
 
0.37301671< 0.1%
 

Surprised
Real number (ℝ≥0)

MISSING
ZEROS

Distinct91379
Distinct (%)74.6%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.1137983767
Minimum0
Maximum0.9927273
Zeros23112
Zeros (%)15.4%
Memory size1.1 MiB
2020-09-19T11:22:02.883621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.775e-07
median0.01290975
Q30.157316975
95-th percentile0.552855205
Maximum0.9927273
Range0.9927273
Interquartile range (IQR)0.1573159975

Descriptive statistics

Standard deviation0.1818011183
Coefficient of variation (CV)1.597572158
Kurtosis2.598267545
Mean0.1137983767
Median Absolute Deviation (MAD)0.01290975
Skewness1.833586731
Sum13946.21867
Variance0.03305164663
MonotocityNot monotonic
2020-09-19T11:22:03.089246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02311215.4%
 
1e-097980.5%
 
2e-093690.2%
 
3e-092680.2%
 
4e-092010.1%
 
5e-091740.1%
 
6e-091340.1%
 
7e-091210.1%
 
8e-091090.1%
 
1.1e-08980.1%
 
9e-09960.1%
 
1e-08900.1%
 
1.6e-0872< 0.1%
 
1.2e-0870< 0.1%
 
1.4e-0866< 0.1%
 
1.3e-0860< 0.1%
 
1.8e-0858< 0.1%
 
1.5e-0856< 0.1%
 
2e-0850< 0.1%
 
2.5e-0847< 0.1%
 
2.1e-0846< 0.1%
 
3.2e-0844< 0.1%
 
2.3e-0842< 0.1%
 
2.2e-0842< 0.1%
 
3.6e-0840< 0.1%
 
Other values (91354)9628964.1%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
02311215.4%
 
1e-097980.5%
 
2e-093690.2%
 
3e-092680.2%
 
4e-092010.1%
 
5e-091740.1%
 
6e-091340.1%
 
7e-091210.1%
 
8e-091090.1%
 
9e-09960.1%
 
ValueCountFrequency (%) 
0.99272731< 0.1%
 
0.93535021< 0.1%
 
0.93489771< 0.1%
 
0.93484931< 0.1%
 
0.93480531< 0.1%
 
0.93472971< 0.1%
 
0.9341941< 0.1%
 
0.93387321< 0.1%
 
0.93369931< 0.1%
 
0.93322341< 0.1%
 

Scared
Real number (ℝ≥0)

MISSING
ZEROS

Distinct82766
Distinct (%)67.5%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.03590559614
Minimum0
Maximum0.7054816
Zeros28871
Zeros (%)19.2%
Memory size1.1 MiB
2020-09-19T11:22:03.340011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13e-09
median0.001368494
Q30.0371501325
95-th percentile0.187558485
Maximum0.7054816
Range0.7054816
Interquartile range (IQR)0.0371501295

Descriptive statistics

Standard deviation0.0721742882
Coefficient of variation (CV)2.010112516
Kurtosis14.99982503
Mean0.03590559614
Median Absolute Deviation (MAD)0.001368494
Skewness3.300858918
Sum4400.302618
Variance0.005209127877
MonotocityNot monotonic
2020-09-19T11:22:03.553696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02887119.2%
 
1e-0910690.7%
 
2e-095170.3%
 
3e-093440.2%
 
4e-092700.2%
 
5e-092270.2%
 
6e-091830.1%
 
7e-091720.1%
 
8e-091440.1%
 
9e-091260.1%
 
1e-081170.1%
 
1.1e-081030.1%
 
1.2e-081020.1%
 
1.3e-08910.1%
 
1.4e-08900.1%
 
1.5e-08850.1%
 
1.7e-08820.1%
 
1.9e-0871< 0.1%
 
1.6e-0871< 0.1%
 
2.1e-0867< 0.1%
 
1.8e-0864< 0.1%
 
2.2e-0861< 0.1%
 
2e-0860< 0.1%
 
2.5e-0858< 0.1%
 
2.8e-0857< 0.1%
 
Other values (82741)8945059.5%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
02887119.2%
 
1e-0910690.7%
 
2e-095170.3%
 
3e-093440.2%
 
4e-092700.2%
 
5e-092270.2%
 
6e-091830.1%
 
7e-091720.1%
 
8e-091440.1%
 
9e-091260.1%
 
ValueCountFrequency (%) 
0.70548161< 0.1%
 
0.70548061< 0.1%
 
0.7046591< 0.1%
 
0.70368251< 0.1%
 
0.70304721< 0.1%
 
0.70005021< 0.1%
 
0.69974081< 0.1%
 
0.69942221< 0.1%
 
0.69933741< 0.1%
 
0.69894491< 0.1%
 

Disgusted
Real number (ℝ≥0)

MISSING
ZEROS

Distinct79311
Distinct (%)64.7%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.009855090066
Minimum0
Maximum0.4488862
Zeros28225
Zeros (%)18.8%
Memory size1.1 MiB
2020-09-19T11:22:03.827876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13e-09
median0.000242716
Q30.007745907
95-th percentile0.05354849
Maximum0.4488862
Range0.4488862
Interquartile range (IQR)0.007745904

Descriptive statistics

Standard deviation0.02424606815
Coefficient of variation (CV)2.460258403
Kurtosis33.06774253
Mean0.009855090066
Median Absolute Deviation (MAD)0.000242716
Skewness4.837116386
Sum1207.760998
Variance0.0005878718206
MonotocityNot monotonic
2020-09-19T11:22:04.032801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02822518.8%
 
1e-0913780.9%
 
2e-096930.5%
 
3e-094630.3%
 
4e-093700.2%
 
5e-092990.2%
 
6e-092400.2%
 
7e-092280.2%
 
8e-091940.1%
 
9e-091690.1%
 
1e-081570.1%
 
1.1e-081450.1%
 
1.2e-081350.1%
 
1.3e-081230.1%
 
1.5e-081190.1%
 
1.4e-081120.1%
 
1.7e-081080.1%
 
1.6e-08930.1%
 
1.9e-08910.1%
 
2e-08880.1%
 
2.2e-08820.1%
 
1.8e-08780.1%
 
2.4e-08770.1%
 
2.1e-0874< 0.1%
 
2.7e-0868< 0.1%
 
Other values (79286)8874359.0%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
02822518.8%
 
1e-0913780.9%
 
2e-096930.5%
 
3e-094630.3%
 
4e-093700.2%
 
5e-092990.2%
 
6e-092400.2%
 
7e-092280.2%
 
8e-091940.1%
 
9e-091690.1%
 
ValueCountFrequency (%) 
0.44888621< 0.1%
 
0.42419311< 0.1%
 
0.34450861< 0.1%
 
0.32314931< 0.1%
 
0.31666631< 0.1%
 
0.30529821< 0.1%
 
0.30339971< 0.1%
 
0.30243831< 0.1%
 
0.29930151< 0.1%
 
0.29661771< 0.1%
 

Contempt
Real number (ℝ≥0)

MISSING
ZEROS

Distinct75738
Distinct (%)61.8%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.01702193229
Minimum0
Maximum0.7360929
Zeros30555
Zeros (%)20.3%
Memory size1.1 MiB
2020-09-19T11:22:04.273421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11e-09
median9.49395e-05
Q30.00852661975
95-th percentile0.102254985
Maximum0.7360929
Range0.7360929
Interquartile range (IQR)0.00852661875

Descriptive statistics

Standard deviation0.04377883934
Coefficient of variation (CV)2.571907736
Kurtosis32.98175284
Mean0.01702193229
Median Absolute Deviation (MAD)9.49395e-05
Skewness4.633098937
Sum2086.071846
Variance0.001916586774
MonotocityNot monotonic
2020-09-19T11:22:04.477020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03055520.3%
 
1e-0915331.0%
 
2e-097490.5%
 
3e-095190.3%
 
4e-093890.3%
 
5e-093180.2%
 
6e-092600.2%
 
7e-092400.2%
 
8e-092100.1%
 
9e-091770.1%
 
1e-081600.1%
 
1.2e-081550.1%
 
1.1e-081520.1%
 
1.4e-081260.1%
 
1.5e-081200.1%
 
1.3e-081170.1%
 
1.7e-081070.1%
 
1.8e-08990.1%
 
1.6e-08980.1%
 
2e-08900.1%
 
1.9e-08880.1%
 
2.2e-08870.1%
 
2.1e-08800.1%
 
2.3e-08780.1%
 
2.5e-08760.1%
 
Other values (75713)8596957.2%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
03055520.3%
 
1e-0915331.0%
 
2e-097490.5%
 
3e-095190.3%
 
4e-093890.3%
 
5e-093180.2%
 
6e-092600.2%
 
7e-092400.2%
 
8e-092100.1%
 
9e-091770.1%
 
ValueCountFrequency (%) 
0.73609291< 0.1%
 
0.73344181< 0.1%
 
0.72774261< 0.1%
 
0.72350981< 0.1%
 
0.72011911< 0.1%
 
0.71601511< 0.1%
 
0.71451< 0.1%
 
0.70539561< 0.1%
 
0.70079261< 0.1%
 
0.69881451< 0.1%
 

Valence
Real number (ℝ)

MISSING

Distinct121609
Distinct (%)99.2%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean-0.1012832004
Minimum-0.9999734
Maximum0.9826003
Zeros278
Zeros (%)0.2%
Memory size1.1 MiB
2020-09-19T11:22:04.771452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-0.9999734
5-th percentile-0.64372033
Q1-0.220288725
median-0.061585745
Q3-0.0008832305
95-th percentile0.427949275
Maximum0.9826003
Range1.9825737
Interquartile range (IQR)0.2194054945

Descriptive statistics

Standard deviation0.2959490345
Coefficient of variation (CV)-2.921995292
Kurtosis1.990424011
Mean-0.1012832004
Median Absolute Deviation (MAD)0.091982645
Skewness0.05669428211
Sum-12412.45877
Variance0.08758583105
MonotocityNot monotonic
2020-09-19T11:22:05.005296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02780.2%
 
-0.999973466< 0.1%
 
-1e-0932< 0.1%
 
-2e-0917< 0.1%
 
-3e-0911< 0.1%
 
1e-099< 0.1%
 
-5e-099< 0.1%
 
-4e-098< 0.1%
 
2e-095< 0.1%
 
-6e-095< 0.1%
 
-8e-095< 0.1%
 
-1.6e-084< 0.1%
 
-1.5e-084< 0.1%
 
-1e-084< 0.1%
 
-2e-084< 0.1%
 
-0.99997314< 0.1%
 
-7e-094< 0.1%
 
-0.99997294< 0.1%
 
-0.99997283< 0.1%
 
-0.11573053< 0.1%
 
-6.8e-083< 0.1%
 
-2.3e-083< 0.1%
 
-0.99997323< 0.1%
 
-0.99997163< 0.1%
 
-1.4e-083< 0.1%
 
Other values (121584)12205881.2%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
-0.999973466< 0.1%
 
-0.99997331< 0.1%
 
-0.99997323< 0.1%
 
-0.99997314< 0.1%
 
-0.9999732< 0.1%
 
-0.99997294< 0.1%
 
-0.99997283< 0.1%
 
-0.99997262< 0.1%
 
-0.99997252< 0.1%
 
-0.99997241< 0.1%
 
ValueCountFrequency (%) 
0.98260031< 0.1%
 
0.9819721< 0.1%
 
0.98178861< 0.1%
 
0.98035761< 0.1%
 
0.97855691< 0.1%
 
0.97799281< 0.1%
 
0.97630261< 0.1%
 
0.9750211< 0.1%
 
0.9738271< 0.1%
 
0.97199481< 0.1%
 

Arousal
Real number (ℝ≥0)

MISSING

Distinct115643
Distinct (%)94.4%
Missing27774
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.3568374717
Minimum0
Maximum0.99
Zeros32
Zeros (%)< 0.1%
Memory size1.1 MiB
2020-09-19T11:22:05.312734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.030164656
Q10.2244962
median0.3677954
Q30.48362425
95-th percentile0.670700725
Maximum0.99
Range0.99
Interquartile range (IQR)0.25912805

Descriptive statistics

Standard deviation0.1907133802
Coefficient of variation (CV)0.5344544656
Kurtosis-0.5156902504
Mean0.3568374717
Median Absolute Deviation (MAD)0.1244427
Skewness-0.005156666891
Sum43731.14583
Variance0.0363715934
MonotocityNot monotonic
2020-09-19T11:22:05.538682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.2475900.1%
 
032< 0.1%
 
0.247499913< 0.1%
 
0.349924310< 0.1%
 
0.26826829< 0.1%
 
0.41604688< 0.1%
 
0.66334147< 0.1%
 
0.69046757< 0.1%
 
0.10383967< 0.1%
 
0.40920086< 0.1%
 
0.37095086< 0.1%
 
0.33696086< 0.1%
 
0.18861826< 0.1%
 
0.034376815< 0.1%
 
0.3523175< 0.1%
 
0.42041195< 0.1%
 
0.24749985< 0.1%
 
0.70635385< 0.1%
 
0.46485895< 0.1%
 
0.64708375< 0.1%
 
0.40476135< 0.1%
 
0.38735545< 0.1%
 
0.41835335< 0.1%
 
0.32876115< 0.1%
 
0.24749965< 0.1%
 
Other values (115618)12228581.3%
 
(Missing)2777418.5%
 
ValueCountFrequency (%) 
032< 0.1%
 
5.1e-081< 0.1%
 
5.2e-081< 0.1%
 
5.4e-081< 0.1%
 
5.5e-081< 0.1%
 
5.6e-081< 0.1%
 
5.7e-081< 0.1%
 
5.8e-081< 0.1%
 
5.9e-081< 0.1%
 
6.1e-081< 0.1%
 
ValueCountFrequency (%) 
0.994< 0.1%
 
0.98995112< 0.1%
 
0.98987871< 0.1%
 
0.98978331< 0.1%
 
0.98975941< 0.1%
 
0.98964221< 0.1%
 
0.98955171< 0.1%
 
0.9891391< 0.1%
 
0.98873841< 0.1%
 
0.98824461< 0.1%
 

Heart_Rate
Real number (ℝ≥0)

MISSING

Distinct64
Distinct (%)0.1%
Missing51848
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean68.26792786
Minimum50
Maximum120
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2020-09-19T11:22:05.764646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile56
Q162
median67
Q373
95-th percentile87
Maximum120
Range70
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.178520149
Coefficient of variation (CV)0.1344484949
Kurtosis0.6596850038
Mean68.26792786
Median Absolute Deviation (MAD)5
Skewness0.8168750356
Sum6722889
Variance84.24523212
MonotocityNot monotonic
2020-09-19T11:22:05.968499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6466814.4%
 
6562124.1%
 
6753463.6%
 
6649263.3%
 
6947493.2%
 
6846863.1%
 
6344923.0%
 
5942042.8%
 
7041302.7%
 
6041292.7%
 
6137792.5%
 
7131702.1%
 
6231662.1%
 
7228671.9%
 
7327871.9%
 
5827311.8%
 
5720181.3%
 
7419401.3%
 
7518581.2%
 
7615961.1%
 
7814741.0%
 
8014341.0%
 
7913780.9%
 
8113760.9%
 
8213710.9%
 
Other values (39)1597810.6%
 
(Missing)5184834.5%
 
ValueCountFrequency (%) 
507180.5%
 
515820.4%
 
523670.2%
 
539470.6%
 
547160.5%
 
5511660.8%
 
5613480.9%
 
5720181.3%
 
5827311.8%
 
5942042.8%
 
ValueCountFrequency (%) 
1201< 0.1%
 
1191< 0.1%
 
1171< 0.1%
 
1151< 0.1%
 
1131< 0.1%
 
1091< 0.1%
 
10710< 0.1%
 
10647< 0.1%
 
10535< 0.1%
 
10432< 0.1%
 

Fecha
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2020-09-19
150326 
ValueCountFrequency (%) 
2020-09-19150326100.0%
 
2020-09-19T11:22:06.169263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-19T11:22:06.270080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:22:06.376785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
045097830.0%
 
230065220.0%
 
-30065220.0%
 
930065220.0%
 
115032610.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number120260880.0%
 
Dash Punctuation30065220.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
045097837.5%
 
230065225.0%
 
930065225.0%
 
115032612.5%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-300652100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1503260100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
045097830.0%
 
230065220.0%
 
-30065220.0%
 
930065220.0%
 
115032610.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1503260100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
045097830.0%
 
230065220.0%
 
-30065220.0%
 
930065220.0%
 
115032610.0%
 

Hora
Categorical

HIGH CARDINALITY
UNIFORM

Distinct59471
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
00:08:01.320000
 
4
00:04:39.120000
 
4
00:16:43.200000
 
4
00:18:08.040000
 
4
00:11:53.400000
 
4
Other values (59466)
150306 
ValueCountFrequency (%) 
00:08:01.3200004< 0.1%
 
00:04:39.1200004< 0.1%
 
00:16:43.2000004< 0.1%
 
00:18:08.0400004< 0.1%
 
00:11:53.4000004< 0.1%
 
00:00:43.6800004< 0.1%
 
00:19:58.0800004< 0.1%
 
00:15:50.0400004< 0.1%
 
00:00:22.2000004< 0.1%
 
00:05:37.4400004< 0.1%
 
00:03:50.0400004< 0.1%
 
00:01:58.2000004< 0.1%
 
00:22:21.6000004< 0.1%
 
00:02:11.0400004< 0.1%
 
00:04:15.2400004< 0.1%
 
00:04:30.8390004< 0.1%
 
00:16:23.0400004< 0.1%
 
00:00:29.5200004< 0.1%
 
00:21:45.2400004< 0.1%
 
00:05:20.6400004< 0.1%
 
00:13:02.5200004< 0.1%
 
00:19:184< 0.1%
 
00:15:52.0800004< 0.1%
 
00:07:08.8800004< 0.1%
 
00:07:08.7600004< 0.1%
 
Other values (59446)15022699.9%
 
2020-09-19T11:22:06.705169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique12279 ?
Unique (%)8.2%
2020-09-19T11:22:06.905499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length15
Mean length14.72680042
Min length8

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0100856745.6%
 
:30065213.6%
 
.1444596.5%
 
21395846.3%
 
11235075.6%
 
41006484.5%
 
3823293.7%
 
8731113.3%
 
6722463.3%
 
5708963.2%
 
9548182.5%
 
7430041.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number176871079.9%
 
Other Punctuation44511120.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0100856757.0%
 
21395847.9%
 
11235077.0%
 
41006485.7%
 
3823294.7%
 
8731114.1%
 
6722464.1%
 
5708964.0%
 
9548183.1%
 
7430042.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:30065267.5%
 
.14445932.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2213821100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0100856745.6%
 
:30065213.6%
 
.1444596.5%
 
21395846.3%
 
11235075.6%
 
41006484.5%
 
3823293.7%
 
8731113.3%
 
6722463.3%
 
5708963.2%
 
9548182.5%
 
7430041.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2213821100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0100856745.6%
 
:30065213.6%
 
.1444596.5%
 
21395846.3%
 
11235075.6%
 
41006484.5%
 
3823293.7%
 
8731113.3%
 
6722463.3%
 
5708963.2%
 
9548182.5%
 
7430041.9%
 

ID
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Control2
54590 
Control1
47192 
Paciente1
35826 
Paciente2
12718 
ValueCountFrequency (%) 
Control25459036.3%
 
Control14719231.4%
 
Paciente13582623.8%
 
Paciente2127188.5%
 
2020-09-19T11:22:07.076436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-19T11:22:07.187035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:22:07.330190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length8
Mean length8.322924843
Min length8

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o20356416.3%
 
n15032612.0%
 
t15032612.0%
 
C1017828.1%
 
r1017828.1%
 
l1017828.1%
 
e970887.8%
 
1830186.6%
 
2673085.4%
 
P485443.9%
 
a485443.9%
 
c485443.9%
 
i485443.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter95050076.0%
 
Uppercase Letter15032612.0%
 
Decimal Number15032612.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C10178267.7%
 
P4854432.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o20356421.4%
 
n15032615.8%
 
t15032615.8%
 
r10178210.7%
 
l10178210.7%
 
e9708810.2%
 
a485445.1%
 
c485445.1%
 
i485445.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
18301855.2%
 
26730844.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin110082688.0%
 
Common15032612.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o20356418.5%
 
n15032613.7%
 
t15032613.7%
 
C1017829.2%
 
r1017829.2%
 
l1017829.2%
 
e970888.8%
 
P485444.4%
 
a485444.4%
 
c485444.4%
 
i485444.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
18301855.2%
 
26730844.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1251152100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o20356416.3%
 
n15032612.0%
 
t15032612.0%
 
C1017828.1%
 
r1017828.1%
 
l1017828.1%
 
e970887.8%
 
1830186.6%
 
2673085.4%
 
P485443.9%
 
a485443.9%
 
c485443.9%
 
i485443.9%
 

ID_Group
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Controles
101782 
Pacientes
48544 
ValueCountFrequency (%) 
Controles10178267.7%
 
Pacientes4854432.3%
 
2020-09-19T11:22:07.493299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-19T11:22:07.595828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:22:07.715878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length9
Min length9

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o20356415.0%
 
e19887014.7%
 
n15032611.1%
 
t15032611.1%
 
s15032611.1%
 
C1017827.5%
 
r1017827.5%
 
l1017827.5%
 
P485443.6%
 
a485443.6%
 
c485443.6%
 
i485443.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter120260888.9%
 
Uppercase Letter15032611.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C10178267.7%
 
P4854432.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o20356416.9%
 
e19887016.5%
 
n15032612.5%
 
t15032612.5%
 
s15032612.5%
 
r1017828.5%
 
l1017828.5%
 
a485444.0%
 
c485444.0%
 
i485444.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1352934100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o20356415.0%
 
e19887014.7%
 
n15032611.1%
 
t15032611.1%
 
s15032611.1%
 
C1017827.5%
 
r1017827.5%
 
l1017827.5%
 
P485443.6%
 
a485443.6%
 
c485443.6%
 
i485443.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1352934100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o20356415.0%
 
e19887014.7%
 
n15032611.1%
 
t15032611.1%
 
s15032611.1%
 
C1017827.5%
 
r1017827.5%
 
l1017827.5%
 
P485443.6%
 
a485443.6%
 
c485443.6%
 
i485443.6%
 

Interactions

2020-09-19T11:21:26.423856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:26.669847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:26.875553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:27.058649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:27.265346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:27.451354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:27.680327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:27.918348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:28.119680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:28.342239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:28.666847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:29.246003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:29.525749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:29.824818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:30.076094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:30.317840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:30.533831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:30.776207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:31.002527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:31.224951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:31.445184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:31.659681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:31.879779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:32.083959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:32.288086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:32.528728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:32.816584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:33.042712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:33.277307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:33.491426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:33.694294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:33.907366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:34.099705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:34.295651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:34.501494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:34.725270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:34.932994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:35.161063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:35.361640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:35.607309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:35.847238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:36.091262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:36.347488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:36.543775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:36.764941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:36.977643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:37.210161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:37.444779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:37.652487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:38.053895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:38.253185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:38.452160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:38.643072image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:38.871222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:39.077129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:39.277789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:39.497767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:39.699585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:39.912623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:40.191443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:40.434456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:40.707986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:40.968690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:41.229246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:41.476963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:41.717220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:41.930647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:42.147158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:42.368118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:42.579402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:42.850564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:43.079873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:43.353794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:43.588118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:43.845332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:44.090202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:44.313010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:44.539041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:44.806167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:45.052514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:45.284012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:45.531035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:45.752112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:45.986485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:46.219283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:46.441286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:46.675270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:46.876563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:47.112774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:47.327277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:47.563467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:47.818835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:48.107367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:48.352404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:48.909901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:49.179878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:49.406149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:49.720909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:49.976426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:50.262297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:50.500093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:50.843896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:51.161191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:51.374942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:51.604636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:51.867937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:52.114647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:52.377928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:52.606043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:52.830752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:53.088148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:53.337649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:53.580258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:53.855053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:54.117318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:54.344734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:54.592801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:54.876134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:55.095404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:55.337309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:55.591438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-19T11:22:07.878897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-19T11:22:08.158720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-19T11:22:08.429019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-19T11:22:08.717747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-19T11:22:08.979459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-19T11:21:56.464039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:57.338651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:58.686498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T11:21:59.286592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

Video_TimeNeutralHappySadAngrySurprisedScaredDisgustedContemptValenceArousalHeart_RateFechaHoraIDID_Group
02020-09-19 00:00:00.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00Control1Controles
12020-09-19 00:00:00.040NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00.040000Control1Controles
22020-09-19 00:00:00.080NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00.080000Control1Controles
32020-09-19 00:00:00.120NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00.120000Control1Controles
42020-09-19 00:00:00.160NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00.160000Control1Controles
52020-09-19 00:00:00.200NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00.200000Control1Controles
62020-09-19 00:00:00.240NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00.240000Control1Controles
72020-09-19 00:00:00.280NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00.280000Control1Controles
82020-09-19 00:00:00.320NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00.320000Control1Controles
92020-09-19 00:00:00.360NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:00:00.360000Control1Controles

Last rows

Video_TimeNeutralHappySadAngrySurprisedScaredDisgustedContemptValenceArousalHeart_RateFechaHoraIDID_Group
1503162020-09-19 00:25:24.959NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:24.959000Paciente2Pacientes
1503172020-09-19 00:25:25.080NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:25.080000Paciente2Pacientes
1503182020-09-19 00:25:25.199NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:25.199000Paciente2Pacientes
1503192020-09-19 00:25:25.320NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:25.320000Paciente2Pacientes
1503202020-09-19 00:25:25.439NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:25.439000Paciente2Pacientes
1503212020-09-19 00:25:25.560NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:25.560000Paciente2Pacientes
1503222020-09-19 00:25:25.679NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:25.679000Paciente2Pacientes
1503232020-09-19 00:25:25.800NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:25.800000Paciente2Pacientes
1503242020-09-19 00:25:25.919NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:25.919000Paciente2Pacientes
1503252020-09-19 00:25:26.040NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2020-09-1900:25:26.040000Paciente2Pacientes